{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RPXZQODZ6EUD4RO4PM3DGXRKBE","short_pith_number":"pith:RPXZQODZ","schema_version":"1.0","canonical_sha256":"8bef983879f1283e45dc7b36335e2a0922288288e0453a13f1ec402f300790f1","source":{"kind":"arxiv","id":"2411.19548","version":1},"attestation_state":"computed","paper":{"title":"ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Chaojun Ni, Chen Liu, Guan Huang, Guosheng Zhao, Kun Zhan, Peng Jia, Wenjun Mei, Wenkang Qin, Xianpeng Lang, Xiaofeng Wang, Xingang Wang, Xueyang Zhang, Yida Wang, Yifei Zhan, Yuyin Chen, Zheng Zhu","submitted_at":"2024-11-29T08:47:46Z","abstract_excerpt":"Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that closely mirror training data distributions. However, these methods struggle with rendering novel trajectories, such as lane changes. Recent works have demonstrated that integrating world model knowledge alleviates these issues. Despite their efficiency, these approaches still encounter difficulties in the accurate representation of more complex maneuvers, with multi-lane shifts being a notable example. Therefore, we in"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.19548","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-29T08:47:46Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"8f18037d0f658c2bf71f0baf055f54d7e509529fcb7d7b7222deb21152dda324","abstract_canon_sha256":"7b90d021d528db8e4df3b01e2e81ea0f2e4ecec4dfa90fdcea48a77932bfb75d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:04.242407Z","signature_b64":"SZqf4Qpw2B23ve36sipQIUeAUh1lzfKodBBohDkuijuOFag+QrSc1lkhbCj93a8i+CTj3RrAMk9TvitPFnV3BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8bef983879f1283e45dc7b36335e2a0922288288e0453a13f1ec402f300790f1","last_reissued_at":"2026-07-05T09:42:04.241886Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:04.241886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Chaojun Ni, Chen Liu, Guan Huang, Guosheng Zhao, Kun Zhan, Peng Jia, Wenjun Mei, Wenkang Qin, Xianpeng Lang, Xiaofeng Wang, Xingang Wang, Xueyang Zhang, Yida Wang, Yifei Zhan, Yuyin Chen, Zheng Zhu","submitted_at":"2024-11-29T08:47:46Z","abstract_excerpt":"Closed-loop simulation is crucial for end-to-end autonomous driving. Existing sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes based on conditions that closely mirror training data distributions. However, these methods struggle with rendering novel trajectories, such as lane changes. Recent works have demonstrated that integrating world model knowledge alleviates these issues. Despite their efficiency, these approaches still encounter difficulties in the accurate representation of more complex maneuvers, with multi-lane shifts being a notable example. Therefore, we in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.19548","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.19548/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.19548","created_at":"2026-07-05T09:42:04.241951+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.19548v1","created_at":"2026-07-05T09:42:04.241951+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.19548","created_at":"2026-07-05T09:42:04.241951+00:00"},{"alias_kind":"pith_short_12","alias_value":"RPXZQODZ6EUD","created_at":"2026-07-05T09:42:04.241951+00:00"},{"alias_kind":"pith_short_16","alias_value":"RPXZQODZ6EUD4RO4","created_at":"2026-07-05T09:42:04.241951+00:00"},{"alias_kind":"pith_short_8","alias_value":"RPXZQODZ","created_at":"2026-07-05T09:42:04.241951+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2603.11755","citing_title":"Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18993","citing_title":"AutoAWG: Adverse Weather Generation with Adaptive Multi-Controls for Automotive Videos","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RPXZQODZ6EUD4RO4PM3DGXRKBE","json":"https://pith.science/pith/RPXZQODZ6EUD4RO4PM3DGXRKBE.json","graph_json":"https://pith.science/api/pith-number/RPXZQODZ6EUD4RO4PM3DGXRKBE/graph.json","events_json":"https://pith.science/api/pith-number/RPXZQODZ6EUD4RO4PM3DGXRKBE/events.json","paper":"https://pith.science/paper/RPXZQODZ"},"agent_actions":{"view_html":"https://pith.science/pith/RPXZQODZ6EUD4RO4PM3DGXRKBE","download_json":"https://pith.science/pith/RPXZQODZ6EUD4RO4PM3DGXRKBE.json","view_paper":"https://pith.science/paper/RPXZQODZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.19548&json=true","fetch_graph":"https://pith.science/api/pith-number/RPXZQODZ6EUD4RO4PM3DGXRKBE/graph.json","fetch_events":"https://pith.science/api/pith-number/RPXZQODZ6EUD4RO4PM3DGXRKBE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RPXZQODZ6EUD4RO4PM3DGXRKBE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RPXZQODZ6EUD4RO4PM3DGXRKBE/action/storage_attestation","attest_author":"https://pith.science/pith/RPXZQODZ6EUD4RO4PM3DGXRKBE/action/author_attestation","sign_citation":"https://pith.science/pith/RPXZQODZ6EUD4RO4PM3DGXRKBE/action/citation_signature","submit_replication":"https://pith.science/pith/RPXZQODZ6EUD4RO4PM3DGXRKBE/action/replication_record"}},"created_at":"2026-07-05T09:42:04.241951+00:00","updated_at":"2026-07-05T09:42:04.241951+00:00"}